1. Technical Field
The present invention relates to the identification or verification of an object class, and relates also to the synthesis of images of objects. The invention relates particularly though not exclusively to the identification or verification of faces, and relates also to the synthesis of images of faces.
2. Related Art
Many known methods of face identification utilise a universal face space model indicative of facial features of a non-homogeneous population. Commonly, the universal face space model is represented as a set of appearance parameters that are best able to represent variations between faces in a restricted dimensional space (see For example U.S. Pat. No. 5,164,992; M. A. Turk and A. P. Pentland). A face to be identified is converted into a set of appearance parameters and then compared with sets of appearance parameters indicative of known faces.
Recently published face identification methods include the active appearance method and active shape method (G. J. Edwards, C. J. Taylor, and T. F. Cootes. Face recognition using Active Appearance Models. In 5th European Conference on Computer Vision, pages 581–595, 1998; T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham. Active Shape Models—their training and application. Computer Vision and Image Understanding, 61(1):38–59, January 1995). The active appearance method comprises a universal face space model with which an unknown face is compared, and further includes pre-learned knowledge indicating how to adjust appearance parameters in universal face space in order to match a face synthesised using the model to an unknown face. Using the pre-learned knowledge is advantageous because it allows the required number adjustment iterations to be minimised.
If the facial appearance of each individual was unchanging, and every image of each individual was identical, then each individual could be represented by a single point in universal face space. However, the facial appearance of an individual may vary in response to a number of factors, for example changes of expression, pose or illumination. The variability of appearance parameters representative of the appearance of an individual under changes of expression, pose, illumination or other factors can be expressed as a probability density function. The probability density function defines a volume in universal face space which is considered to correspond to a given individual. So for example, a series of images of an individual, with a variety of expressions should all fall within the volume described by the probability density function in universal face space.
In known face identification methods a single probability density function is generated which is applied to all individuals. This is done centering the probability density function on a mean parameter vector for a given individual.